Understanding the math behind deep learning is important if you want to become proficient in machine learning techniques.

We just published a 14-hour deep learning course on the freeCodeCamp.org YouTube channel. This course is specifically tailored to transform beginners into proficient developers in the field of deep learning. Whether you're looking to understand the core concepts or want to apply deep learning to real-world challenges, this course has got you covered.

Ayush Singh developed this course. He is an expereinced data scientist and popular course creator.

What You Will Learn

The course starts with foundational topics and gradually advances through more complex concepts. Here’s what you can expect:

  • Fundamental Concepts: Start from the basics of vectors and matrices and understand their operations.

  • Deep Learning Techniques: Learn about the architectures and applications of deep learning, including neural networks and the backpropagation algorithm.

  • Mathematical Insights: Dive into linear algebra and calculus that underpin machine learning algorithms, covering topics from determinants to differentiation and integration.

  • Practical Applications: Discover how these techniques apply to real-world problems across various industries.

Course Structure

The course is structured to ensure a clear and practical learning journey:

  1. Basics of Algebra: Understand vectors, matrices, and their operations.

  2. Linear Algebra Concepts: Explore complex topics such as determinants, the rule of Sarrus, and the cofactor of matrices.

  3. Calculus: Grasp essential calculus concepts that are crucial for deep learning.

  4. Deep Learning and Machine Learning: Delve into the core of deep learning and machine learning, learning how these technologies can be applied to design intelligent systems.

Starting with the basics, the course introduces learners to the core concepts of vectors and matrices before diving deeper into operations like matrix multiplication, determinants, and the geometric implications of these concepts. Each section is designed to build on the previous one, ensuring a comprehensive understanding of the subject matter.

The course also goes into the practical applications of calculus in deep learning, covering key topics such as limits, continuity, differentiation, and integration. It provides a robust precalculus foundation, including a review of trigonometric functions, ensuring that learners are well-prepared to tackle more complex subjects.

In the later sections, the course introduces the fundamental principles of machine learning and deep learning, exploring the architecture of neural networks, the significance of activation functions, and the backpropagation algorithm essential for training deep learning models.

Watch the full course on the freeCodeCamp.org YouTube channel (14-hour watch).